Find out what your peers are saying about IBM, Exabeam, Cynet and others in User Entity Behavior Analytics (UEBA).
Instead of starting with a massive pool of data and then mining it for usable threat intelligence, we first build a system for transforming human expertise into models that can evaluate complex security problems. We then apply diverse data to the model. With further analytics we can automatically score the highest-priority threat signals and rapidly deliver them to the right people at the right time. We have also built a tightly integrated ‘ecosystem’ of web and mobile apps to enable our users to manage their critical assets and incident responses.
Splunk User Behavior Analytics is a behavior-based threat detection is based on machine learning methodologies that require no signatures or human analysis, enabling multi-entity behavior profiling and peer group analytics for users, devices, service accounts and applications. It detects insider threats and external attacks using out-of-the-box purpose-built that helps organizations find known, unknown and hidden threats, but extensible unsupervised machine learning (ML) algorithms, provides context around the threat via ML driven anomaly correlation and visual mapping of stitched anomalies over various phases of the attack lifecycle (Kill-Chain View). It uses a data science driven approach that produces actionable results with risk ratings and supporting evidence that increases SOC efficiency and supports bi-directional integration with Splunk Enterprise for data ingestion and correlation and with Splunk Enterprise Security for incident scoping, workflow management and automated response. The result is automated, accurate threat and anomaly detection.
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